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Diagnosing error trade-offs when modifying network size and regularization
Case context: You are training a neural network that currently suffers from high variance (overfitting). A team member proposes two independent solutions to evaluate: first, significantly increasing the size of the neural network by adding more layers; second, introducing L2 regularization to the current model.
Question: Based on the bias-variance tradeoff, analyze the expected impact of each proposed solution on the model's bias and variance. Which change is more appropriate for addressing the high variance?
Sample answer: Increasing the neural network size (adding layers) generally reduces bias but could further increase variance, which would worsen the current problem. Conversely, adding regularization generally increases bias but reduces variance, making it the appropriate choice to address the high variance issue.
Key points:
- Increasing the size of the model (adding layers) generally reduces bias but could increase variance.
- Adding regularization generally increases bias but reduces variance.
- To address high variance, adding regularization is the appropriate choice.
Rubric: The learner must identify that increasing network size reduces bias but increases variance, worsening the issue. They must also identify that adding regularization reduces variance but increases bias, which directly addresses the high variance error.
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